Network structure exploration in networks with node attributes

Yi Chen, Xiaolong Wang, Junzhao Bu, Buzhou Tang and Xin Xiang

Physica A: Statistical Mechanics and its Applications, 2016, vol. 449, issue C, 240-253

Abstract: Complex networks provide a powerful way to represent complex systems and have been widely studied during the past several years. One of the most important tasks of network analysis is to detect structures (also called structural regularities) embedded in networks by determining group number and group partition. Most of network structure exploration models only consider network links. However, in real world networks, nodes may have attributes that are useful for network structure exploration. In this paper, we propose a novel Bayesian nonparametric (BNP) model to explore structural regularities in networks with node attributes, called Bayesian nonparametric attribute (BNPA) model. This model does not only take full advantage of both links between nodes and node attributes for group partition via shared hidden variables, but also determine group number automatically via the Bayesian nonparametric theory. Experiments conducted on a number of real and synthetic networks show that our BNPA model is able to automatically explore structural regularities in networks with node attributes and is competitive with other state-of-the-art models.

Keywords: Network structure; Structure exploration; Node attributes; Bayesian nonparametric model (search for similar items in EconPapers)
Date: 2016
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:449:y:2016:i:c:p:240-253

DOI: 10.1016/j.physa.2015.12.133

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